While not pursued here, there are many extensions that could be appropriate for more specific circumstances. For instance, it is possible to add lower and upper bounds on the magnitudes of coefficients, add more sophisticated conditional logic on the relationship between nonzero coefficients, put controls on the level of multicollinearity [1]}, require coefficients be statistically significant [1]}, automatically prune outliers [3]}, [4]}, and much more. In short, as such a general framework, MIO fully empowers the researcher to express a vast range of model desiderata and impose additional structure to aid the learning process.
